



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	a      :  Union[str, Any]			     =			'platform'

	import jax
	import jax.numpy as jnp

	from transformers import BlenderbotTokenizer
	from transformers.models.blenderbot.modeling_flax_blenderbot import (
	    FlaxBlenderbotForConditionalGeneration,
	    FlaxBlenderbotModel,
	    shift_tokens_right,
	)







def  lowerCAmelCase_	(lowerCAmelCase__:		int		,						lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Tuple=None		,						lowerCAmelCase__:		Tuple=None		,						lowerCAmelCase__:		str=None		,						lowerCAmelCase__:		str=None		,						lowerCAmelCase__:		Optional[int]=None		,						lowerCAmelCase__:		List[Any]=None		,						):





			"""simple docstring"""


			if attention_mask is None:
						UpperCAmelCase_:		Optional[int]							      =     np.where(input_ids != config.pad_token_id		,						1		,						0  )
			if decoder_attention_mask is None:
						UpperCAmelCase_:		Union[str, Any]							      =     np.where(decoder_input_ids != config.pad_token_id		,						1		,						0  )
			if head_mask is None:
						UpperCAmelCase_:		List[str]							      =     np.ones((config.encoder_layers, config.encoder_attention_heads)  )
			if decoder_head_mask is None:
						UpperCAmelCase_:		Union[str, Any]							      =     np.ones((config.decoder_layers, config.decoder_attention_heads)  )
			if cross_attn_head_mask is None:
						UpperCAmelCase_:		Optional[int]							      =     np.ones((config.decoder_layers, config.decoder_attention_heads)  )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": attention_mask,
			}







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=13,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=0.0_2,   )       ->   Any:
						UpperCAmelCase_:		Optional[int]							      =     parent
						UpperCAmelCase_:		Optional[int]							      =     batch_size
						UpperCAmelCase_:		Any							      =     seq_length
						UpperCAmelCase_:		Optional[Any]							      =     is_training
						UpperCAmelCase_:		Dict							      =     use_labels
						UpperCAmelCase_:		int							      =     vocab_size
						UpperCAmelCase_:		List[Any]							      =     hidden_size
						UpperCAmelCase_:		Any							      =     num_hidden_layers
						UpperCAmelCase_:		List[str]							      =     num_attention_heads
						UpperCAmelCase_:		Optional[int]							      =     intermediate_size
						UpperCAmelCase_:		int							      =     hidden_act
						UpperCAmelCase_:		List[str]							      =     hidden_dropout_prob
						UpperCAmelCase_:		Optional[Any]							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		str							      =     max_position_embeddings
						UpperCAmelCase_:		List[str]							      =     eos_token_id
						UpperCAmelCase_:		Optional[Any]							      =     pad_token_id
						UpperCAmelCase_:		Union[str, Any]							      =     bos_token_id
						UpperCAmelCase_:		int							      =     initializer_range

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Any							      =     np.clip(ids_tensor([self.batch_size, self.seq_length - 1],   self.vocab_size					),   3,   self.vocab_size					)
						UpperCAmelCase_:		Any							      =     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),   dtype=np.intaa					)),   -1					)

						UpperCAmelCase_:		str							      =     shift_tokens_right(SCREAMING_SNAKE_CASE_,   1,   2					)

						UpperCAmelCase_:		int							      =     BlenderbotConfig(
						    vocab_size=self.vocab_size,   d_model=self.hidden_size,   encoder_layers=self.num_hidden_layers,   decoder_layers=self.num_hidden_layers,   encoder_attention_heads=self.num_attention_heads,   decoder_attention_heads=self.num_attention_heads,   encoder_ffn_dim=self.intermediate_size,   decoder_ffn_dim=self.intermediate_size,   dropout=self.hidden_dropout_prob,   attention_dropout=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   eos_token_id=self.eos_token_id,   bos_token_id=self.bos_token_id,   pad_token_id=self.pad_token_id,   initializer_range=self.initializer_range,   use_cache=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		Any							      =     prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						return config, inputs_dict

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     self.prepare_config_and_inputs()
						return config, inputs_dict

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						UpperCAmelCase_:		Tuple							      =     20
						UpperCAmelCase_:		Any							      =     model_class_name(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[Any]							      =     model.encode(inputs_dict["""input_ids"""]					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     (
						    inputs_dict["""decoder_input_ids"""],
						    inputs_dict["""decoder_attention_mask"""],
						)

						UpperCAmelCase_:		Tuple							      =     model.init_cache(decoder_input_ids.shape[0],   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length),   dtype="""i4"""					)

						UpperCAmelCase_:		Optional[Any]							      =     jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :],   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),   )
						UpperCAmelCase_:		Union[str, Any]							      =     model.decode(
						    decoder_input_ids[:, :-1],   SCREAMING_SNAKE_CASE_,   decoder_attention_mask=SCREAMING_SNAKE_CASE_,   past_key_values=SCREAMING_SNAKE_CASE_,   decoder_position_ids=SCREAMING_SNAKE_CASE_,   )

						UpperCAmelCase_:		str							      =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],   dtype="""i4"""					)
						UpperCAmelCase_:		Union[str, Any]							      =     model.decode(
						    decoder_input_ids[:, -1:],   SCREAMING_SNAKE_CASE_,   decoder_attention_mask=SCREAMING_SNAKE_CASE_,   past_key_values=outputs_cache.past_key_values,   decoder_position_ids=SCREAMING_SNAKE_CASE_,   )

						UpperCAmelCase_:		Optional[int]							      =     model.decode(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
						self.parent.assertTrue(diff < 1E-3,   msg=f'Max diff is {diff}'					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   int:
						UpperCAmelCase_:		Optional[int]							      =     20
						UpperCAmelCase_:		Any							      =     model_class_name(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[int]							      =     model.encode(inputs_dict["""input_ids"""]					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		Dict							      =     (
						    inputs_dict["""decoder_input_ids"""],
						    inputs_dict["""decoder_attention_mask"""],
						)

						UpperCAmelCase_:		Dict							      =     jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
						    ],   axis=-1,   )

						UpperCAmelCase_:		str							      =     model.init_cache(decoder_input_ids.shape[0],   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :],   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),   )

						UpperCAmelCase_:		Optional[int]							      =     model.decode(
						    decoder_input_ids[:, :-1],   SCREAMING_SNAKE_CASE_,   decoder_attention_mask=SCREAMING_SNAKE_CASE_,   past_key_values=SCREAMING_SNAKE_CASE_,   decoder_position_ids=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		Union[str, Any]							      =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],   dtype="""i4"""					)
						UpperCAmelCase_:		str							      =     model.decode(
						    decoder_input_ids[:, -1:],   SCREAMING_SNAKE_CASE_,   past_key_values=outputs_cache.past_key_values,   decoder_attention_mask=SCREAMING_SNAKE_CASE_,   decoder_position_ids=SCREAMING_SNAKE_CASE_,   )

						UpperCAmelCase_:		List[Any]							      =     model.decode(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   decoder_attention_mask=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[Any]							      =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
						self.parent.assertTrue(diff < 1E-3,   msg=f'Max diff is {diff}'					)







@require_flax
class 				_a				(   unittest.TestCase					):
			A      		=	99

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Any							      =     np.array(
						    [
						        [71, 82, 18, 33, 46, 91, 2],
						        [68, 34, 26, 58, 30, 82, 2],
						        [5, 97, 17, 39, 94, 40, 2],
						        [76, 83, 94, 25, 70, 78, 2],
						        [87, 59, 41, 35, 48, 66, 2],
						        [55, 13, 16, 58, 5, 2, 1],  # note padding
						        [64, 27, 31, 51, 12, 75, 2],
						        [52, 64, 86, 17, 83, 39, 2],
						        [48, 61, 9, 24, 71, 82, 2],
						        [26, 1, 60, 48, 22, 13, 2],
						        [21, 5, 62, 28, 14, 76, 2],
						        [45, 98, 37, 86, 59, 48, 2],
						        [70, 70, 50, 9, 28, 0, 2],
						    ],   dtype=np.intaa,   )

						UpperCAmelCase_:		Any							      =     input_ids.shape[0]
						UpperCAmelCase_:		List[str]							      =     BlenderbotConfig(
						    vocab_size=self.vocab_size,   d_model=24,   encoder_layers=2,   decoder_layers=2,   encoder_attention_heads=2,   decoder_attention_heads=2,   encoder_ffn_dim=32,   decoder_ffn_dim=32,   max_position_embeddings=48,   eos_token_id=2,   pad_token_id=1,   bos_token_id=0,   )
						return config, input_ids, batch_size

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     self._get_config_and_data()
						UpperCAmelCase_:		Optional[Any]							      =     FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     lm_model(input_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Any							      =     BlenderbotConfig(
						    vocab_size=self.vocab_size,   d_model=14,   encoder_layers=2,   decoder_layers=2,   encoder_attention_heads=2,   decoder_attention_heads=2,   encoder_ffn_dim=8,   decoder_ffn_dim=8,   max_position_embeddings=48,   )
						UpperCAmelCase_:		int							      =     FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],   dtype=np.intaa					)
						UpperCAmelCase_:		int							      =     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],   dtype=np.intaa					)
						UpperCAmelCase_:		str							      =     lm_model(input_ids=SCREAMING_SNAKE_CASE_,   decoder_input_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     (*summary.shape, config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Optional[int]							      =     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],   dtype=np.intaa					)
						UpperCAmelCase_:		List[Any]							      =     shift_tokens_right(SCREAMING_SNAKE_CASE_,   1,   2					)
						UpperCAmelCase_:		Optional[Any]							      =     np.equal(SCREAMING_SNAKE_CASE_,   1					).astype(np.floataa					).sum()
						UpperCAmelCase_:		List[str]							      =     np.equal(SCREAMING_SNAKE_CASE_,   1					).astype(np.floataa					).sum()
						self.assertEqual(shifted.shape,   input_ids.shape					)
						self.assertEqual(SCREAMING_SNAKE_CASE_,   n_pad_before - 1					)
						self.assertTrue(np.equal(shifted[:, 0],   2					).all()					)







@require_flax
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase ,		_lowerCAmelCase					):
			A      		=	True
			A      		=	(
			    (
			        FlaxBlenderbotModel,
			        FlaxBlenderbotForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			A      		=	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		List[str]							      =     FlaxBlenderbotModelTester(self					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__					):
												UpperCAmelCase_:		Tuple							      =     self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[Any]							      =     model_class(SCREAMING_SNAKE_CASE_					)

												@jax.jit
												def encode_jitted(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_					):
															return model.encode(input_ids=SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_					)

												with self.subTest("""JIT Enabled"""					):
															UpperCAmelCase_:		int							      =     encode_jitted(**SCREAMING_SNAKE_CASE_					).to_tuple()

												with self.subTest("""JIT Disabled"""					):
															with jax.disable_jit():
																		UpperCAmelCase_:		Optional[int]							      =     encode_jitted(**SCREAMING_SNAKE_CASE_					).to_tuple()

												self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   len(SCREAMING_SNAKE_CASE_					)					)
												for jitted_output, output in zip(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
															self.assertEqual(jitted_output.shape,   output.shape					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__					):
												UpperCAmelCase_:		Optional[int]							      =     model_class(SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		str							      =     model.encode(inputs_dict["""input_ids"""],   inputs_dict["""attention_mask"""]					)

												UpperCAmelCase_:		Any							      =     {
												    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
												    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
												    """encoder_outputs""": encoder_outputs,
												}

												@jax.jit
												def decode_jitted(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
															return model.decode(
															    decoder_input_ids=SCREAMING_SNAKE_CASE_,   decoder_attention_mask=SCREAMING_SNAKE_CASE_,   encoder_outputs=SCREAMING_SNAKE_CASE_,   )

												with self.subTest("""JIT Enabled"""					):
															UpperCAmelCase_:		Optional[int]							      =     decode_jitted(**SCREAMING_SNAKE_CASE_					).to_tuple()

												with self.subTest("""JIT Disabled"""					):
															with jax.disable_jit():
																		UpperCAmelCase_:		List[str]							      =     decode_jitted(**SCREAMING_SNAKE_CASE_					).to_tuple()

												self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   len(SCREAMING_SNAKE_CASE_					)					)
												for jitted_output, output in zip(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
															self.assertEqual(jitted_output.shape,   output.shape					)

			@slow
			def      __snake_case       (self					)       ->   List[str]:
						for model_class_name in self.all_model_classes:
									UpperCAmelCase_:		Tuple							      =     model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""					)
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									UpperCAmelCase_:		Tuple							      =     np.ones((1, 1)					) * model.config.eos_token_id
									UpperCAmelCase_:		Any							      =     model(SCREAMING_SNAKE_CASE_					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)

			@unittest.skipUnless(jax_device != """cpu""",   """3B test too slow on CPU."""					)
			@slow
			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Union[str, Any]							      =     {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
						UpperCAmelCase_:		List[Any]							      =     {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

						UpperCAmelCase_:		str							      =     FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""",   from_pt=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""					)

						UpperCAmelCase_:		int							      =     ["""Sam"""]
						UpperCAmelCase_:		Any							      =     tokenizer(SCREAMING_SNAKE_CASE_,   return_tensors="""jax"""					)

						UpperCAmelCase_:		Optional[int]							      =     model.generate(**SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     """Sam is a great name. It means \"sun\" in Gaelic."""

						UpperCAmelCase_:		List[Any]							      =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)
						assert generated_txt[0].strip() == tgt_text











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  List[Any]			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''timm_backbone'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_,   )       ->   Tuple:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     backbone
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		Optional[Any]							      =     features_only
						UpperCAmelCase_:		Any							      =     use_pretrained_backbone
						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     out_indices if out_indices is not None else (-1,)






